Synthetic Data Generation for Rare Disease Research
Keywords:
Generative AI Models, Data Sources, Generative Model Types, Data Generation Metrics, Biases, and Capability, Generative AI Model Evaluation in Healthcare.Abstract
Shallow Learning agents require abundant labelled data to excel. In rare diseases, where patient population is often limited to a few hundred patients, such a data source is not readily available. Generative AI models can synthesize new, similar data by learning an approximate distribution of the original data. Their application in rare disease cohorts and clinical data is detailed, with supervisory signals derived from the data labelling mechanisms. The response from Generative AI models is further validated by comparing model predictions with real-world outcomes.
Rare diseases are conditions that affect a small percentage of the population — definitions suggest a prevalence of below 1:2000 but this varies among nations. Globally, a collection of more than 7000 rare diseases, affecting 25 million patients, has been enumerated, with the rapid emergence of new entities identified. Early diagnosis, clinical management, and developing novel therapeutics for rare diseases offer challenging problems with limited labelled data-sets available to Deep Learning agents. Synthetic Data Generation, by augmentation or providing an alternative data source, enables Shallow Learning agents to attempt these tasks across phenotype, genotype, and narrative data.
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